The study analyses Flickr posts in an attempt to identify posts made by users that are committing self harm. They look at many metrics - tags, photo attributes (arousal, contrast, dominance, etc), post content (verbs, nouns, adverbs, readability, and sentiment), as well as times that posts are made. All of these metrics are compared with "normal" users. After determining where the main differences between self-harming users and normal users are, they conducted an experimental analysis with their algorithm. Their empirical analysis found that their algorithms would indeed help identify self-harm posts.

They also propose both a supervised and unsupervised algorithm. The difference being that in a supervised scenario, there are labels to help guide the machine learning process. However, since it is expensive and time consuming to label social media posts, the authors also propose an unsupervised algorithm. These sections have a lot of math notation, so if you are interested in the specifics of the algorithm, you should read the paper.